Generative AI Implementation for Production-Ready Enterprise Systems
We design, build, and operate generative AI implementation programs that turn LLM pilots into governed, production-grade products: custom AI agents, RAG pipelines, evaluation frameworks, and MCP-based integrations into your systems of record. From first MVP to enterprise rollout, we deliver measurable business impact in 6-10 weeks on AWS, GCP, or Azure.
Ship GenAI apps and agents your compliance, security, and engineering teams will actually approve.
- Custom LLM apps, RAG systems, and agentic workflows on AWS, GCP, or Azure
- LLMOps foundation: evaluation harness, prompt versioning, guardrails, observability
- MCP-based tool integration with CRM, ERP, data warehouse, and internal APIs
- Model routing across OpenAI, Anthropic, Google, and open-source LLMs (Llama, Mistral)
- 6-10 weeks from discovery to first production-grade GenAI use case
Why Do Most Generative AI Pilots Fail to Reach Production?
Most organisations have built at least one GenAI demo: a chatbot, a document summariser, a copilot. Very few run generative AI in software development or customer-facing systems at scale. The blocker is rarely the model. It is the absence of evaluation standards, grounding discipline, integration governance, and an operating model that treats GenAI as a product, not a prototype.
Architecture & Technical Building Blocks
Routing across OpenAI, Anthropic, Google, AWS Bedrock, and self-hosted open-source LLMs.
pgvector, Pinecone, OpenSearch, and Vertex AI with hybrid search and re-ranking.
Event-driven orchestration for multi-step agents with retries, timeouts, and circuit breakers.
Prompt registry and evaluation harness wired into CI/CD for every model or prompt change.
Token usage, latency p95/p99, groundedness scores, and cost per transaction.
Data residency options and VPC-isolated deployments for regulated workloads.
How We Deliver Generative AI Implementation: From Discovery to Run
We qualify 1-3 use cases against business value, data readiness, and compliance constraints. Output: prioritised backlog, target SLOs, evaluation criteria, and reference architecture. (1-2 weeks)
We deploy the GenAI platform: model gateway, vector store, prompt registry, evaluation harness, observability. Output: a reusable platform ready for multiple apps and agents. (2-3 weeks)
We build the first GenAI app or agent, grounded in your data, with automated evaluation and guardrails. Output: a use case hitting pre-agreed accuracy, latency, and cost targets. (3-4 weeks)
We ship to production behind feature flags, run canary rollout, and validate business KPIs. Output: first implementation live with SLOs, dashboards, and on-call runbooks. (Week 6-10)
We provide SLA-based support, model refreshes, prompt tuning, and enablement so your teams progressively own the platform and add new use cases without external dependency.
Business Outcomes From a Production-Grade GenAI Platform
40-70% reduction in manual effort for knowledge-heavy workflows (support, research, documentation)
30-50% ticket deflection on generative AI for customer support deployments
2-5x faster time-to-market for new GenAI use cases after the platform is in place
30-60% lower token and inference cost through model routing and caching
6-10 weeks from kickoff to first production use case, not 6-12 months
Who This Generative AI Service Is For
End-to-End Generative AI Software Development Services
We cover the full path from first MVP to enterprise rollout: custom GenAI apps and RAG systems, AI agent development and deployment, the LLMOps layer that keeps them operable, MCP integration with your systems of record, and the evaluation and guardrails that gate every release on measurable quality.
Frequently Asked Questions
6-10 weeks from discovery to first production use case. The first 2-3 weeks go into the LLMOps platform and evaluation harness; weeks 3-6 cover the MVP build; weeks 6-10 cover hardening, canary rollout, and go-live. Platform investment is reused by every subsequent use case, which is why the second and third apps typically ship in 3-5 weeks.
GenAI app development usually means single-turn or short-turn systems (summarisers, Q&A, content generation) where the model produces output in response to a prompt. Custom AI agent development services cover multi-step systems where the LLM plans, calls tools, observes results, and loops until a task is complete. Agents need extra discipline around tool schemas, guardrails, memory, and safe escalation.
Both. We design model-agnostic architectures with a routing layer, so you can mix OpenAI, Anthropic, Google, and self-hosted open-source models (Llama, Mistral, Qwen) per use case. Open-source models on your cloud make sense for data residency, cost at scale, or fine-tuning; commercial APIs win on frontier capability and time-to-value. We help you decide per workload.
With grounding, evaluation, and guardrails. Every use case ships with a retrieval layer over approved data sources, an evaluation dataset with accuracy and groundedness thresholds, automated regression tests on each prompt or model change, and runtime guardrails that block unsafe outputs. Releases are gated by measurable quality, not subjective review.
Yes. We use Model Context Protocol (MCP) and standardised tool schemas to integrate with Salesforce, HubSpot, ServiceNow, Zendesk, SAP, Snowflake, BigQuery, Databricks, and custom internal APIs. Integrations respect your IAM, logging, and data residency requirements, and every tool call is auditable.
AI agent deployment uses the same discipline as any other production service: CI/CD with automated evaluation, canary rollout behind feature flags, SLO-based monitoring (latency, cost, groundedness, tool success rate), kill-switches, and rollback. Agents run in isolated environments with scoped credentials and are observable end-to-end, from user prompt to tool call to final response.
Yes. You own the code, prompts, evaluation datasets, and infrastructure. Everything is built on your cloud account (AWS, GCP, or Azure) under your IAM, and we hand over documentation, runbooks, and knowledge transfer so your team can operate independently. We stay engaged through an SLA only as long as you want us to.
Ready to Move Generative AI From Pilot to Production?
Book a 30-minute, no-obligation scoping call. We will review your use case shortlist, data readiness, and target architecture, and give you a realistic implementation plan with timelines, costs, and measurable outcomes, whether or not you decide to work with us.
Discovery call
A 30-minute, no-obligation call to review your use case shortlist and data readiness.
Implementation plan
We hand you a realistic plan with timelines, costs, and target architecture.
Build & go-live
First production use case live in 6-10 weeks with SLOs, dashboards, and runbooks.